暂无分享,去创建一个
Eren Erdal Aksoy | Tomas Nordström | Ahmed Hemani | Nesma M. Rezk | Dimitrios Stathis | Zain Ul-Abdin
[1] Ahmed Hemani,et al. MOCHA: Morphable Locality and Compression Aware Architecture for Convolutional Neural Networks , 2017, 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS).
[2] Tianqi Chen,et al. Net2Net: Accelerating Learning via Knowledge Transfer , 2015, ICLR.
[3] Syed M. A. H. Jafri,et al. The SiLago Solution: Architecture and Design Methods for a Heterogeneous Dark Silicon Aware Coarse Grain Reconfigurable Fabric , 2017 .
[4] Zhongyang Zheng,et al. Research Advance in Swarm Robotics , 2013 .
[5] Tarek F. Abdelzaher,et al. DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework , 2017, SenSys.
[6] Bo Chen,et al. NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications , 2018, ECCV.
[7] Zhiru Zhang,et al. Improving Neural Network Quantization without Retraining using Outlier Channel Splitting , 2019, ICML.
[8] Kalyanmoy Deb,et al. Pymoo: Multi-Objective Optimization in Python , 2020, IEEE Access.
[9] Madhura Purnaprajna,et al. Recurrent Neural Networks: An Embedded Computing Perspective , 2019, IEEE Access.
[10] David E. Goldberg,et al. A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.
[11] Yu Zhang,et al. Simple Recurrent Units for Highly Parallelizable Recurrence , 2017, EMNLP.
[12] Guillaume Gravier,et al. A Step Beyond Local Observations with a Dialog Aware Bidirectional GRU Network for Spoken Language Understanding , 2016, INTERSPEECH.
[13] Avi Mendelson,et al. Loss Aware Post-training Quantization , 2019, ArXiv.
[14] Chan Mo Kim,et al. Multiplier design based on ancient Indian Vedic Mathematics , 2008, 2008 International SoC Design Conference.
[15] Ji Liu,et al. End-to-End Learning of Energy-Constrained Deep Neural Networks , 2018, ArXiv.
[16] Vivienne Sze,et al. Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Andrew W. Senior,et al. Long short-term memory recurrent neural network architectures for large scale acoustic modeling , 2014, INTERSPEECH.
[18] Yoshua Bengio,et al. BinaryConnect: Training Deep Neural Networks with binary weights during propagations , 2015, NIPS.
[19] Patrick Judd,et al. Stripes: Bit-serial deep neural network computing , 2016, 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).
[20] Kalyanmoy Deb,et al. MULTI-OBJECTIVE FUNCTION OPTIMIZATION USING NON-DOMINATED SORTING GENETIC ALGORITHMS , 1994 .
[21] Titouan Parcollet,et al. The Pytorch-kaldi Speech Recognition Toolkit , 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[22] Dejan S. Milojicic,et al. PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference , 2019, ASPLOS.
[23] Kenneth Heafield,et al. Neural Machine Translation with 4-Bit Precision and Beyond , 2019, ArXiv.
[24] Vijay Kumar,et al. A review on genetic algorithm: past, present, and future , 2020, Multimedia tools and applications.
[25] Rana Ali Amjad,et al. Up or Down? Adaptive Rounding for Post-Training Quantization , 2020, ICML.
[26] Zhijian Liu,et al. HAQ: Hardware-Aware Automated Quantization With Mixed Precision , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Niraj K. Jha,et al. NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm , 2017, IEEE Transactions on Computers.
[28] Yousra Alkabani,et al. A distributed genetic algorithm for swarm robots obstacle avoidance , 2014, 2014 9th International Conference on Computer Engineering & Systems (ICCES).
[29] Daniel Soudry,et al. Post training 4-bit quantization of convolutional networks for rapid-deployment , 2018, NeurIPS.
[30] Liang Qiao,et al. Optimizing Speech Recognition For The Edge , 2019, ArXiv.
[31] Trevor Darrell,et al. Long-term recurrent convolutional networks for visual recognition and description , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Christos-Savvas Bouganis,et al. Approximate FPGA-based LSTMs under Computation Time Constraints , 2018, ARC.
[33] Suyog Gupta,et al. To prune, or not to prune: exploring the efficacy of pruning for model compression , 2017, ICLR.
[34] Daniel Povey,et al. The Kaldi Speech Recognition Toolkit , 2011 .
[35] Ahmed Hemani,et al. Parallel distributed scalable runtime address generation scheme for a coarse grain reconfigurable computation and storage fabric , 2014, Microprocess. Microsystems.
[36] Hannu Tenhunen,et al. Private configuration environments (PCE) for efficient reconfiguration, in CGRAs , 2013, 2013 IEEE 24th International Conference on Application-Specific Systems, Architectures and Processors.
[37] Hoi-Jun Yoo,et al. UNPU: An Energy-Efficient Deep Neural Network Accelerator With Fully Variable Weight Bit Precision , 2019, IEEE Journal of Solid-State Circuits.
[38] David Thorsley,et al. Post-training Piecewise Linear Quantization for Deep Neural Networks , 2020, ECCV.
[39] Jonathan G. Fiscus,et al. DARPA TIMIT:: acoustic-phonetic continuous speech corpus CD-ROM, NIST speech disc 1-1.1 , 1993 .
[40] Dragan Savic,et al. Single-objective vs. Multiobjective Optimisation for Integrated Decision Support , 2002 .
[41] Ji Li,et al. Image describing based on bidirectional LSTM and improved sequence sampling , 2017, 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA)(.
[42] Kurt Keutzer,et al. ZeroQ: A Novel Zero Shot Quantization Framework , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[43] Yu Zhang,et al. Training RNNs as Fast as CNNs , 2017, EMNLP 2018.
[44] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[45] Kalyanmoy Deb,et al. A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..
[46] Peeter Ellervee,et al. TransMem: A memory architecture to support dynamic remapping and parallelism in low power high performance CGRAs , 2016, 2016 26th International Workshop on Power and Timing Modeling, Optimization and Simulation (PATMOS).
[47] Hadi Esmaeilzadeh,et al. Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network , 2017, 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA).
[48] Wonyong Sung,et al. Resiliency of Deep Neural Networks under Quantization , 2015, ArXiv.
[49] Qinru Qiu,et al. C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs , 2018, FPGA.
[50] Ahmed Hemani,et al. Partially reconfigurable interconnection network for dynamically reprogrammable resource array , 2009, 2009 IEEE 8th International Conference on ASIC.
[51] Maurizio Martina,et al. NACU: A Non-Linear Arithmetic Unit for Neural Networks , 2020, 2020 57th ACM/IEEE Design Automation Conference (DAC).
[52] Norbert Wehn,et al. FINN-L: Library Extensions and Design Trade-Off Analysis for Variable Precision LSTM Networks on FPGAs , 2018, 2018 28th International Conference on Field Programmable Logic and Applications (FPL).
[53] Azlan Mohd Zain,et al. Overview of NSGA-II for Optimizing Machining Process Parameters , 2011 .